Improved resolution from subpixel shifted pictures
CVGIP: Graphical Models and Image Processing
Super-Resolution Imaging
Motion-Free Super-Resolution
Single frame image super-resolution: should we process locally or globally?
Multidimensional Systems and Signal Processing
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
Edge-Model based representation of laplacian subbands
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast computation of edge model representation for image sequence super-resolution
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
| Hi-index | 0.00 |
The present paper proposes a new method for high resolution image generation from a single image. Generation of high resolution (HR) images from lower resolution image(s) is achieved by either reconstruction-based methods or by learning-based methods. Reconstruction based methods use multiple images of the same scene to gather the extra information needed for the HR. The learning-based methods rely on the learning of characteristics of a specific image set to inject the extra information for HR generation. The proposed method is a variation of this strategy. It uses a generative model for sharp edges in images as well as descriptive models for edge representation. This prior information is injected using the Symmetric Residue Pyramid scheme. The advantages of this scheme are that it generates sharp edges with no ringing artefacts in the HR and that the models are universal enough to allow usage on wide variety of images without requirement of training and/or adaptation. Results have been generated and compared to actual high resolution images. Index terms: Super-Resolution, edge modelling, Laplacian pyramids.